View-sharing for 4D magnetic resonance imaging with randomized projection-encoding enables improvements of respiratory motion imaging for treatment planning in abdominothoracic radiotherapy. (January 2023)

• Record Type: Journal Article
• Authors: Subashi, Ergys; Feng, Li; Liu, Yilin; Robertson, Scott; Segars, Paul; Driehuys, Bastiaan; Kelsey, Christopher R.; Yin, Fang-Fang; Otazo, Ricardo; Cai, Jing
• Is Part Of: Physics and imaging in radiation oncology. Volume 25(2023)
• Publication Date: 2023-01
• Subjects: Respiratory imaging -- 4D-MRI -- Projection-encoding -- View-sharing; Radiotherapy -- Periodicals; Radiation dosimetry -- Periodicals; Cancer -- Imaging -- Periodicals; Oncology -- Periodicals; 615.842
• DOI: 10.1016/j.phro.2022.12.006
• Languages: English
• ISSNs: 2405-6316

Abstract

Highlights: The acquisition of respiratory phase-resolved 4D magnetic resonance imaging (MRI) with sufficiently high spatiotemporal resolution and coverage remains challenging. This work demonstrates a 4D-MRI technique for improving the spatiotemporal resolution of respiratory motion imaging. The method defines a sampling function based on projection-encoding and peripheral k-space view-sharing. The respiratory signal is used to optimize spatial resolution and minimize temporal blurring by limiting the extent of data sharing in the Fourier domain. Abstract: Background and Purpose: The accuracy and precision of radiation therapy are dependent on the characterization of organ-at-risk and target motion. This work aims to demonstrate a 4D magnetic resonance imaging (MRI) method for improving spatial and temporal resolution in respiratory motion imaging for treatment planning in abdominothoracic radiotherapy. Materials and Methods: The spatial and temporal resolution of phase-resolved respiratory imaging is improved by considering a novel sampling function based on quasi-random projection-encoding and peripheral k-space view-sharing. The respiratory signal is determined directly from k-space, obviating the need for an external surrogate marker. The average breathing curve is used to optimize spatial resolution and temporal blurring by limiting the extent of data sharing in the Fourier domain. Improvements in image quality are characterized by evaluating changes in signal-to-noise ratio (SNR), resolution, target detection, and level of artifact. The method is validated in simulations, in a dynamic phantom, and in-vivo imaging. Results: Sharing of high-frequency k-space data, driven by the average breathing curve, improves spatial resolution and reduces artifacts. Although equal sharing of k-space data improves resolution and SNR in stationary features, phases with large temporal changes accumulate significant artifacts due to averaging of high frequency features. In the absence of view-sharing, no averaging and detection artifacts are observed while spatial resolution is degraded. Conclusions: The use of a quasi-random sampling function, with view-sharing driven by the average breathing curve, provides a feasible method for self-navigated 4D-MRI at improved spatial resolution.